Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations2405
Missing cells12288
Missing cells (%)23.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory682.5 B

Variable types

Text2
DateTime1
Categorical8
Numeric10
Unsupported1

Alerts

cant_MontoLimite has constant value "1.0" Constant
cluster_k_4 has constant value "3" Constant
anio_preinscripcion is highly overall correlated with antiguedad and 2 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 2 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_antecedentes and 3 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with anio_preinscripcion and 5 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_suspensiones is highly overall correlated with anio_preinscripcion and 6 other fieldsHigh correlation
monto_total_adjudicado is highly overall correlated with cant_antecedentes and 2 other fieldsHigh correlation
total_articulos_provee is highly overall correlated with cant_suspensionesHigh correlation
Estado is highly imbalanced (64.4%) Imbalance
cant_socios has 297 (12.3%) missing values Missing
cant_apercibimientos has 2405 (100.0%) missing values Missing
cant_suspensiones has 2396 (99.6%) missing values Missing
cant_antecedentes has 2393 (99.5%) missing values Missing
cant_Apoderado has 252 (10.5%) missing values Missing
cant_representante has 788 (32.8%) missing values Missing
cant_noAutenticado has 1321 (54.9%) missing values Missing
cant_MontoLimite has 2401 (99.8%) missing values Missing
monto_total_adjudicado is highly skewed (γ1 = 23.18817223) Skewed
CUIT has unique values Unique
cant_apercibimientos is an unsupported type, check if it needs cleaning or further analysis Unsupported
antiguedad has 199 (8.3%) zeros Zeros

Reproduction

Analysis started2025-06-24 13:33:05.023480
Analysis finished2025-06-24 13:33:15.689393
Duration10.67 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct2405
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size178.4 KiB
2025-06-24T10:33:15.803492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length11
Mean length10.979626
Min length8

Characters and Unicode

Total characters26406
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2405 ?
Unique (%)100.0%

Sample

1st row20305924076
2nd row30710210000
3rd row33714924619
4th row33561600959
5th row30694465591
ValueCountFrequency (%)
30711585423 1
 
< 0.1%
30518773743 1
 
< 0.1%
20305924076 1
 
< 0.1%
30710210000 1
 
< 0.1%
33714924619 1
 
< 0.1%
30714480347 1
 
< 0.1%
30515838399 1
 
< 0.1%
30714864137 1
 
< 0.1%
30711301638 1
 
< 0.1%
30639415550 1
 
< 0.1%
Other values (2395) 2395
99.6%
2025-06-24T10:33:16.046388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4578
17.3%
3 3966
15.0%
7 3079
11.7%
1 2719
10.3%
6 2262
8.6%
5 2111
8.0%
2 2005
7.6%
9 1998
7.6%
8 1836
7.0%
4 1791
 
6.8%
Other values (21) 61
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4578
17.3%
3 3966
15.0%
7 3079
11.7%
1 2719
10.3%
6 2262
8.6%
5 2111
8.0%
2 2005
7.6%
9 1998
7.6%
8 1836
7.0%
4 1791
 
6.8%
Other values (21) 61
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4578
17.3%
3 3966
15.0%
7 3079
11.7%
1 2719
10.3%
6 2262
8.6%
5 2111
8.0%
2 2005
7.6%
9 1998
7.6%
8 1836
7.0%
4 1791
 
6.8%
Other values (21) 61
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4578
17.3%
3 3966
15.0%
7 3079
11.7%
1 2719
10.3%
6 2262
8.6%
5 2111
8.0%
2 2005
7.6%
9 1998
7.6%
8 1836
7.0%
4 1791
 
6.8%
Other values (21) 61
 
0.2%

Nombre
Text

Distinct2362
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size211.2 KiB
2025-06-24T10:33:16.238029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length122
Median length75
Mean length21.168815
Min length2

Characters and Unicode

Total characters50911
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2360 ?
Unique (%)98.1%

Sample

1st rowSuministros EDA
2nd rowTECNARAN SRL
3rd rowSIGNIFY ARGENTINA S.A.
4th rowRognoni y CIA SA
5th rowADSUR S.A..
ValueCountFrequency (%)
s.a 695
 
8.8%
srl 433
 
5.5%
sa 322
 
4.1%
de 286
 
3.6%
s.r.l 236
 
3.0%
argentina 153
 
1.9%
y 149
 
1.9%
la 63
 
0.8%
servicios 61
 
0.8%
del 48
 
0.6%
Other values (3331) 5437
69.0%
2025-06-24T10:33:16.567945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5478
 
10.8%
A 4355
 
8.6%
S 3652
 
7.2%
R 2719
 
5.3%
E 2654
 
5.2%
I 2501
 
4.9%
. 2387
 
4.7%
O 2129
 
4.2%
L 1995
 
3.9%
N 1841
 
3.6%
Other values (82) 21200
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5478
 
10.8%
A 4355
 
8.6%
S 3652
 
7.2%
R 2719
 
5.3%
E 2654
 
5.2%
I 2501
 
4.9%
. 2387
 
4.7%
O 2129
 
4.2%
L 1995
 
3.9%
N 1841
 
3.6%
Other values (82) 21200
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5478
 
10.8%
A 4355
 
8.6%
S 3652
 
7.2%
R 2719
 
5.3%
E 2654
 
5.2%
I 2501
 
4.9%
. 2387
 
4.7%
O 2129
 
4.2%
L 1995
 
3.9%
N 1841
 
3.6%
Other values (82) 21200
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5478
 
10.8%
A 4355
 
8.6%
S 3652
 
7.2%
R 2719
 
5.3%
E 2654
 
5.2%
I 2501
 
4.9%
. 2387
 
4.7%
O 2129
 
4.2%
L 1995
 
3.9%
N 1841
 
3.6%
Other values (82) 21200
41.6%
Distinct990
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size37.6 KiB
Minimum2016-01-08 00:00:00
Maximum2022-12-09 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-24T10:33:16.652119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:16.767415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

Imbalance 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size183.7 KiB
Inscripto
1904 
Desactualizado Por Documentos Vencidos
 
157
Pre Inscripto
 
142
Desactualizado Por Mantencion Formulario
 
140
Desactualizado Por Clase
 
32
Other values (5)
 
30

Length

Max length40
Median length9
Mean length13.214137
Min length9

Characters and Unicode

Total characters31780
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowInscripto
2nd rowInscripto
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 1904
79.2%
Desactualizado Por Documentos Vencidos 157
 
6.5%
Pre Inscripto 142
 
5.9%
Desactualizado Por Mantencion Formulario 140
 
5.8%
Desactualizado Por Clase 32
 
1.3%
En Evaluacion 17
 
0.7%
Con Solicitud De Baja 10
 
0.4%
Inhabilitado 1
 
< 0.1%
Suspendido 1
 
< 0.1%
Dar De Baja 1
 
< 0.1%

Length

2025-06-24T10:33:16.870698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:33:17.167184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 2046
57.6%
desactualizado 329
 
9.3%
por 329
 
9.3%
documentos 157
 
4.4%
vencidos 157
 
4.4%
pre 142
 
4.0%
mantencion 140
 
3.9%
formulario 140
 
3.9%
clase 32
 
0.9%
en 17
 
0.5%
Other values (8) 62
 
1.7%

Most occurring characters

ValueCountFrequency (%)
o 3634
11.4%
c 2856
9.0%
i 2852
9.0%
n 2826
8.9%
r 2798
8.8%
s 2722
8.6%
t 2683
8.4%
I 2047
 
6.4%
p 2047
 
6.4%
a 1358
 
4.3%
Other values (20) 5957
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3634
11.4%
c 2856
9.0%
i 2852
9.0%
n 2826
8.9%
r 2798
8.8%
s 2722
8.6%
t 2683
8.4%
I 2047
 
6.4%
p 2047
 
6.4%
a 1358
 
4.3%
Other values (20) 5957
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3634
11.4%
c 2856
9.0%
i 2852
9.0%
n 2826
8.9%
r 2798
8.8%
s 2722
8.6%
t 2683
8.4%
I 2047
 
6.4%
p 2047
 
6.4%
a 1358
 
4.3%
Other values (20) 5957
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3634
11.4%
c 2856
9.0%
i 2852
9.0%
n 2826
8.9%
r 2798
8.8%
s 2722
8.6%
t 2683
8.4%
I 2047
 
6.4%
p 2047
 
6.4%
a 1358
 
4.3%
Other values (20) 5957
18.7%

TipoSocietario
Categorical

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size264.2 KiB
Sociedad Anónima
1127 
Sociedad Responsabilidad Limitada
699 
Persona Física
210 
Otras Formas Societarias
 
107
Organismo Publico
 
86
Other values (6)
176 

Length

Max length40
Median length38
Mean length21.958004
Min length12

Characters and Unicode

Total characters52809
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowPersona Física
2nd rowSociedad Responsabilidad Limitada
3rd rowSociedad Anónima
4th rowSociedad Anónima
5th rowSociedad Anónima

Common Values

ValueCountFrequency (%)
Sociedad Anónima 1127
46.9%
Sociedad Responsabilidad Limitada 699
29.1%
Persona Física 210
 
8.7%
Otras Formas Societarias 107
 
4.4%
Organismo Publico 86
 
3.6%
Persona Jurídica Extranjero Sin Sucursal 80
 
3.3%
Cooperativas 51
 
2.1%
Sociedades De Hecho 39
 
1.6%
Unión Transitoria de Empresas 4
 
0.2%
Persona Física Extranjero No Residente 1
 
< 0.1%

Length

2025-06-24T10:33:17.290098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sociedad 1826
31.2%
anónima 1127
19.2%
responsabilidad 699
 
11.9%
limitada 699
 
11.9%
persona 291
 
5.0%
física 211
 
3.6%
otras 107
 
1.8%
formas 107
 
1.8%
societarias 107
 
1.8%
organismo 86
 
1.5%
Other values (17) 597
 
10.2%

Most occurring characters

ValueCountFrequency (%)
a 7160
13.6%
i 6611
12.5%
d 5915
11.2%
n 3505
 
6.6%
o 3471
 
6.6%
3452
 
6.5%
e 3226
 
6.1%
s 2492
 
4.7%
c 2470
 
4.7%
S 2132
 
4.0%
Other values (28) 12375
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7160
13.6%
i 6611
12.5%
d 5915
11.2%
n 3505
 
6.6%
o 3471
 
6.6%
3452
 
6.5%
e 3226
 
6.1%
s 2492
 
4.7%
c 2470
 
4.7%
S 2132
 
4.0%
Other values (28) 12375
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7160
13.6%
i 6611
12.5%
d 5915
11.2%
n 3505
 
6.6%
o 3471
 
6.6%
3452
 
6.5%
e 3226
 
6.1%
s 2492
 
4.7%
c 2470
 
4.7%
S 2132
 
4.0%
Other values (28) 12375
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7160
13.6%
i 6611
12.5%
d 5915
11.2%
n 3505
 
6.6%
o 3471
 
6.6%
3452
 
6.5%
e 3226
 
6.1%
s 2492
 
4.7%
c 2470
 
4.7%
S 2132
 
4.0%
Other values (28) 12375
23.4%

anio_preinscripcion
Categorical

High correlation 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size162.1 KiB
2017
996 
2016
446 
2018
396 
2020
188 
2019
180 
Other values (2)
199 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters9620
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 996
41.4%
2016 446
18.5%
2018 396
 
16.5%
2020 188
 
7.8%
2019 180
 
7.5%
2021 130
 
5.4%
2022 69
 
2.9%

Length

2025-06-24T10:33:17.373274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:33:17.445012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 996
41.4%
2016 446
18.5%
2018 396
 
16.5%
2020 188
 
7.8%
2019 180
 
7.5%
2021 130
 
5.4%
2022 69
 
2.9%

Most occurring characters

ValueCountFrequency (%)
2 2861
29.7%
0 2593
27.0%
1 2148
22.3%
7 996
 
10.4%
6 446
 
4.6%
8 396
 
4.1%
9 180
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2861
29.7%
0 2593
27.0%
1 2148
22.3%
7 996
 
10.4%
6 446
 
4.6%
8 396
 
4.1%
9 180
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2861
29.7%
0 2593
27.0%
1 2148
22.3%
7 996
 
10.4%
6 446
 
4.6%
8 396
 
4.1%
9 180
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2861
29.7%
0 2593
27.0%
1 2148
22.3%
7 996
 
10.4%
6 446
 
4.6%
8 396
 
4.1%
9 180
 
1.9%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct113
Distinct (%)4.7%
Missing17
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean10.788107
Minimum1
Maximum468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:17.545585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q39
95-th percentile45
Maximum468
Range467
Interquartile range (IQR)8

Descriptive statistics

Standard deviation28.787629
Coefficient of variation (CV)2.6684597
Kurtosis96.495999
Mean10.788107
Median Absolute Deviation (MAD)2
Skewness8.4093271
Sum25762
Variance828.7276
MonotonicityNot monotonic
2025-06-24T10:33:17.652595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 744
30.9%
2 346
14.4%
3 237
 
9.9%
4 152
 
6.3%
5 95
 
4.0%
6 94
 
3.9%
7 63
 
2.6%
8 52
 
2.2%
9 48
 
2.0%
11 47
 
2.0%
Other values (103) 510
21.2%
ValueCountFrequency (%)
1 744
30.9%
2 346
14.4%
3 237
 
9.9%
4 152
 
6.3%
5 95
 
4.0%
6 94
 
3.9%
7 63
 
2.6%
8 52
 
2.2%
9 48
 
2.0%
10 43
 
1.8%
ValueCountFrequency (%)
468 1
< 0.1%
455 1
< 0.1%
384 1
< 0.1%
365 1
< 0.1%
332 1
< 0.1%
298 1
< 0.1%
291 1
< 0.1%
277 1
< 0.1%
267 1
< 0.1%
227 1
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation  Skewed 

Distinct2368
Distinct (%)99.2%
Missing17
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.7554632 × 108
Minimum0
Maximum4.617215 × 1010
Zeros16
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:17.765337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48533.308
Q11261255.2
median8483399.7
Q342766677
95-th percentile5.3246566 × 108
Maximum4.617215 × 1010
Range4.617215 × 1010
Interquartile range (IQR)41505422

Descriptive statistics

Standard deviation1.3080865 × 109
Coefficient of variation (CV)7.4515178
Kurtosis705.25475
Mean1.7554632 × 108
Median Absolute Deviation (MAD)8250045.3
Skewness23.188172
Sum4.1920461 × 1011
Variance1.7110903 × 1018
MonotonicityNot monotonic
2025-06-24T10:33:17.870474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
0.7%
1419000 5
 
0.2%
510000 2
 
0.1%
4068972.973 1
 
< 0.1%
4214057.143 1
 
< 0.1%
657495.0324 1
 
< 0.1%
1781374.865 1
 
< 0.1%
544764 1
 
< 0.1%
2248163.265 1
 
< 0.1%
6600857.143 1
 
< 0.1%
Other values (2358) 2358
98.0%
(Missing) 17
 
0.7%
ValueCountFrequency (%)
0 16
0.7%
1.7 1
 
< 0.1%
36.13714286 1
 
< 0.1%
516.24 1
 
< 0.1%
805.37 1
 
< 0.1%
1190 1
 
< 0.1%
2240 1
 
< 0.1%
2412.02 1
 
< 0.1%
2450 1
 
< 0.1%
2662 1
 
< 0.1%
ValueCountFrequency (%)
4.617215015 × 10101
< 0.1%
2.22605735 × 10101
< 0.1%
1.917565525 × 10101
< 0.1%
1.397951455 × 10101
< 0.1%
1.219233471 × 10101
< 0.1%
1.12638198 × 10101
< 0.1%
1.004316276 × 10101
< 0.1%
7675861678 1
< 0.1%
6782343325 1
< 0.1%
6458785714 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3056133
Minimum0
Maximum5
Zeros199
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:17.946118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.481777
Coefficient of variation (CV)0.44826085
Kurtosis-0.10533571
Mean3.3056133
Median Absolute Deviation (MAD)1
Skewness-0.9470501
Sum7950
Variance2.1956632
MonotonicityNot monotonic
2025-06-24T10:33:18.008845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 996
41.4%
5 446
18.5%
3 396
 
16.5%
0 199
 
8.3%
1 188
 
7.8%
2 180
 
7.5%
ValueCountFrequency (%)
0 199
 
8.3%
1 188
 
7.8%
2 180
 
7.5%
3 396
 
16.5%
4 996
41.4%
5 446
18.5%
ValueCountFrequency (%)
5 446
18.5%
4 996
41.4%
3 396
 
16.5%
2 180
 
7.5%
1 188
 
7.8%
0 199
 
8.3%

provincia
Categorical

Distinct27
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size272.2 KiB
Ciudad Autónoma de Buenos Aires
1184 
Buenos Aires
440 
Córdoba
142 
Santa Fe
 
108
Extranjera
 
81
Other values (22)
450 

Length

Max length31
Median length19
Mean length20.167568
Min length5

Characters and Unicode

Total characters48503
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCiudad Autónoma de Buenos Aires
2nd rowCiudad Autónoma de Buenos Aires
3rd rowBuenos Aires
4th rowBuenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 1184
49.2%
Buenos Aires 440
 
18.3%
Córdoba 142
 
5.9%
Santa Fe 108
 
4.5%
Extranjera 81
 
3.4%
Mendoza 73
 
3.0%
Tierra del Fuego 42
 
1.7%
Chubut 32
 
1.3%
Neuquén 30
 
1.2%
Entre Rios 27
 
1.1%
Other values (17) 246
 
10.2%

Length

2025-06-24T10:33:18.097952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aires 1624
20.5%
buenos 1624
20.5%
autónoma 1184
15.0%
ciudad 1184
15.0%
de 1184
15.0%
córdoba 142
 
1.8%
santa 125
 
1.6%
fe 108
 
1.4%
extranjera 81
 
1.0%
mendoza 73
 
0.9%
Other values (28) 590
 
7.5%

Most occurring characters

ValueCountFrequency (%)
5514
11.4%
e 4981
10.3%
u 4301
 
8.9%
d 3821
 
7.9%
s 3369
 
6.9%
a 3278
 
6.8%
n 3277
 
6.8%
o 3238
 
6.7%
i 2993
 
6.2%
A 2808
 
5.8%
Other values (30) 10923
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5514
11.4%
e 4981
10.3%
u 4301
 
8.9%
d 3821
 
7.9%
s 3369
 
6.9%
a 3278
 
6.8%
n 3277
 
6.8%
o 3238
 
6.7%
i 2993
 
6.2%
A 2808
 
5.8%
Other values (30) 10923
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5514
11.4%
e 4981
10.3%
u 4301
 
8.9%
d 3821
 
7.9%
s 3369
 
6.9%
a 3278
 
6.8%
n 3277
 
6.8%
o 3238
 
6.7%
i 2993
 
6.2%
A 2808
 
5.8%
Other values (30) 10923
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5514
11.4%
e 4981
10.3%
u 4301
 
8.9%
d 3821
 
7.9%
s 3369
 
6.9%
a 3278
 
6.8%
n 3277
 
6.8%
o 3238
 
6.7%
i 2993
 
6.2%
A 2808
 
5.8%
Other values (30) 10923
22.5%

cant_socios
Real number (ℝ)

Missing 

Distinct18
Distinct (%)0.9%
Missing297
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean2.341556
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:18.171416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum23
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7698739
Coefficient of variation (CV)0.75585376
Kurtosis25.529278
Mean2.341556
Median Absolute Deviation (MAD)1
Skewness3.8995916
Sum4936
Variance3.1324536
MonotonicityNot monotonic
2025-06-24T10:33:18.254840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 808
33.6%
1 670
27.9%
3 320
 
13.3%
4 153
 
6.4%
5 76
 
3.2%
6 33
 
1.4%
7 13
 
0.5%
8 8
 
0.3%
14 5
 
0.2%
10 4
 
0.2%
Other values (8) 18
 
0.7%
(Missing) 297
 
12.3%
ValueCountFrequency (%)
1 670
27.9%
2 808
33.6%
3 320
 
13.3%
4 153
 
6.4%
5 76
 
3.2%
6 33
 
1.4%
7 13
 
0.5%
8 8
 
0.3%
9 4
 
0.2%
10 4
 
0.2%
ValueCountFrequency (%)
23 1
 
< 0.1%
17 3
0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 5
0.2%
13 2
 
0.1%
12 2
 
0.1%
11 4
0.2%
10 4
0.2%
9 4
0.2%

cant_apercibimientos
Unsupported

Missing  Rejected  Unsupported 

Missing2405
Missing (%)100.0%
Memory size37.6 KiB

cant_suspensiones
Categorical

High correlation  Missing 

Distinct2
Distinct (%)22.2%
Missing2396
Missing (%)99.6%
Memory size150.3 KiB
1.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8
 
0.3%
2.0 1
 
< 0.1%
(Missing) 2396
99.6%

Length

2025-06-24T10:33:18.348640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:33:18.408966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8
88.9%
2.0 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
1 8
29.6%
2 1
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
1 8
29.6%
2 1
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
1 8
29.6%
2 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 9
33.3%
0 9
33.3%
1 8
29.6%
2 1
 
3.7%

cant_antecedentes
Categorical

High correlation  Missing 

Distinct2
Distinct (%)16.7%
Missing2393
Missing (%)99.5%
Memory size150.4 KiB
1.0
10 
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
0.4%
2.0 2
 
0.1%
(Missing) 2393
99.5%

Length

2025-06-24T10:33:18.472668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:33:18.537345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
83.3%
2.0 2
 
16.7%

Most occurring characters

ValueCountFrequency (%)
. 12
33.3%
0 12
33.3%
1 10
27.8%
2 2
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 12
33.3%
0 12
33.3%
1 10
27.8%
2 2
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 12
33.3%
0 12
33.3%
1 10
27.8%
2 2
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 12
33.3%
0 12
33.3%
1 10
27.8%
2 2
 
5.6%

cant_Apoderado
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)0.6%
Missing252
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean1.701347
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:18.596925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0290768
Coefficient of variation (CV)0.60486006
Kurtosis19.924656
Mean1.701347
Median Absolute Deviation (MAD)0
Skewness3.3256667
Sum3663
Variance1.0589991
MonotonicityNot monotonic
2025-06-24T10:33:18.667591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1106
46.0%
2 781
32.5%
3 169
 
7.0%
4 57
 
2.4%
5 20
 
0.8%
6 5
 
0.2%
7 5
 
0.2%
8 4
 
0.2%
11 2
 
0.1%
10 2
 
0.1%
Other values (2) 2
 
0.1%
(Missing) 252
 
10.5%
ValueCountFrequency (%)
1 1106
46.0%
2 781
32.5%
3 169
 
7.0%
4 57
 
2.4%
5 20
 
0.8%
6 5
 
0.2%
7 5
 
0.2%
8 4
 
0.2%
9 1
 
< 0.1%
10 2
 
0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 2
 
0.1%
10 2
 
0.1%
9 1
 
< 0.1%
8 4
 
0.2%
7 5
 
0.2%
6 5
 
0.2%
5 20
 
0.8%
4 57
 
2.4%
3 169
7.0%

cant_representante
Real number (ℝ)

Missing 

Distinct6
Distinct (%)0.4%
Missing788
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean1.2702536
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:18.729663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.57747487
Coefficient of variation (CV)0.45461386
Kurtosis35.889824
Mean1.2702536
Median Absolute Deviation (MAD)0
Skewness3.8935101
Sum2054
Variance0.33347723
MonotonicityNot monotonic
2025-06-24T10:33:18.794370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1250
52.0%
2 315
 
13.1%
3 41
 
1.7%
4 9
 
0.4%
5 1
 
< 0.1%
10 1
 
< 0.1%
(Missing) 788
32.8%
ValueCountFrequency (%)
1 1250
52.0%
2 315
 
13.1%
3 41
 
1.7%
4 9
 
0.4%
5 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
5 1
 
< 0.1%
4 9
 
0.4%
3 41
 
1.7%
2 315
 
13.1%
1 1250
52.0%

cant_autenticado
Real number (ℝ)

Distinct9
Distinct (%)0.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.7699667
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:18.856994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80979018
Coefficient of variation (CV)0.45751718
Kurtosis19.653606
Mean1.7699667
Median Absolute Deviation (MAD)0
Skewness2.6444851
Sum4255
Variance0.65576013
MonotonicityNot monotonic
2025-06-24T10:33:18.920140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 1235
51.4%
1 914
38.0%
3 195
 
8.1%
4 37
 
1.5%
5 15
 
0.6%
6 4
 
0.2%
11 2
 
0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
1 914
38.0%
2 1235
51.4%
3 195
 
8.1%
4 37
 
1.5%
5 15
 
0.6%
6 4
 
0.2%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 2
 
0.1%
ValueCountFrequency (%)
11 2
 
0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 4
 
0.2%
5 15
 
0.6%
4 37
 
1.5%
3 195
 
8.1%
2 1235
51.4%
1 914
38.0%

cant_noAutenticado
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)0.9%
Missing1321
Missing (%)54.9%
Infinite0
Infinite (%)0.0%
Mean1.3487085
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:18.982033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92477353
Coefficient of variation (CV)0.6856734
Kurtosis34.772416
Mean1.3487085
Median Absolute Deviation (MAD)0
Skewness4.8554939
Sum1462
Variance0.85520609
MonotonicityNot monotonic
2025-06-24T10:33:19.053243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 859
35.7%
2 145
 
6.0%
3 54
 
2.2%
5 8
 
0.3%
6 8
 
0.3%
4 6
 
0.2%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
(Missing) 1321
54.9%
ValueCountFrequency (%)
1 859
35.7%
2 145
 
6.0%
3 54
 
2.2%
4 6
 
0.2%
5 8
 
0.3%
6 8
 
0.3%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 8
 
0.3%
5 8
 
0.3%
4 6
 
0.2%
3 54
 
2.2%
2 145
 
6.0%
1 859
35.7%

cant_sinMontoLimite
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3754678
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:19.122287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile4
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92613033
Coefficient of variation (CV)0.38987282
Kurtosis34.511311
Mean2.3754678
Median Absolute Deviation (MAD)0
Skewness4.6797065
Sum5713
Variance0.85771739
MonotonicityNot monotonic
2025-06-24T10:33:19.185597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 1825
75.9%
3 383
 
15.9%
4 118
 
4.9%
5 37
 
1.5%
6 14
 
0.6%
1 10
 
0.4%
7 7
 
0.3%
8 3
 
0.1%
12 3
 
0.1%
11 2
 
0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
1 10
 
0.4%
2 1825
75.9%
3 383
 
15.9%
4 118
 
4.9%
5 37
 
1.5%
6 14
 
0.6%
7 7
 
0.3%
8 3
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 3
 
0.1%
11 2
 
0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 3
 
0.1%
7 7
 
0.3%
6 14
 
0.6%
5 37
 
1.5%
4 118
4.9%

cant_MontoLimite
Categorical

Constant  Missing 

Distinct1
Distinct (%)25.0%
Missing2401
Missing (%)99.8%
Memory size150.3 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 4
 
0.2%
(Missing) 2401
99.8%

Length

2025-06-24T10:33:19.270391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:33:19.301638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct294
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.161331
Minimum1
Maximum4867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.6 KiB
2025-06-24T10:33:19.370537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q334
95-th percentile236.6
Maximum4867
Range4866
Interquartile range (IQR)32

Descriptive statistics

Standard deviation161.13078
Coefficient of variation (CV)3.0890849
Kurtosis345.37784
Mean52.161331
Median Absolute Deviation (MAD)8
Skewness13.736437
Sum125448
Variance25963.128
MonotonicityNot monotonic
2025-06-24T10:33:19.480676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 445
 
18.5%
2 186
 
7.7%
4 132
 
5.5%
3 124
 
5.2%
5 86
 
3.6%
7 76
 
3.2%
6 71
 
3.0%
9 55
 
2.3%
8 54
 
2.2%
11 50
 
2.1%
Other values (284) 1126
46.8%
ValueCountFrequency (%)
1 445
18.5%
2 186
7.7%
3 124
 
5.2%
4 132
 
5.5%
5 86
 
3.6%
6 71
 
3.0%
7 76
 
3.2%
8 54
 
2.2%
9 55
 
2.3%
10 38
 
1.6%
ValueCountFrequency (%)
4867 1
< 0.1%
1732 1
< 0.1%
1414 1
< 0.1%
1272 1
< 0.1%
1078 1
< 0.1%
982 1
< 0.1%
939 1
< 0.1%
912 1
< 0.1%
893 2
0.1%
890 1
< 0.1%

cluster_k_4
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
3
2405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2405
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 2405
100.0%

Length

2025-06-24T10:33:19.570387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-24T10:33:19.618033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2405
100.0%

Most occurring characters

ValueCountFrequency (%)
3 2405
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2405
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2405
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2405
100.0%

Interactions

2025-06-24T10:33:14.174603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.040361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.034266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.878950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.745846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.663887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.486106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:11.722217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.538527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.351944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.279600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.130990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.140343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.035416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.834406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.748343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.570815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:11.805243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.623237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.441015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.387328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.207266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.229507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.119524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.908590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.826552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.645721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:11.867823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.707926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.525745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.485971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.306456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.311388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.199193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.993324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.905687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.737390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:11.952143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.788013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.610368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.590998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.425939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.387259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.275978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.087840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.988933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.829697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.036704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.869246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.688492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.676581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.541720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.470470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.360607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-24T10:33:10.080716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-24T10:33:12.105876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.939424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.767753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-24T10:33:06.644315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.537170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.438723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.297390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.153825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:11.394687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.186319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.032696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.846140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.838057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.740746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.611920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.507926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.394156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.243129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:11.467991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.270529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.108866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.926861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.929777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.837071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.698674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.586190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.494686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.316729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:11.551609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.353336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.186995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.014048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:15.046187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:06.944770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:07.803923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:08.676991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:09.571773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:10.401277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:11.636178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:12.438067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:13.272381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-24T10:33:14.092260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-24T10:33:19.671301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_antecedentescant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesmonto_total_adjudicadoprovinciatotal_articulos_provee
Estado1.0000.2130.1240.1370.0000.4620.0530.0690.0000.0320.0050.0000.0000.0000.1900.000
TipoSocietario0.2131.0000.1420.1510.0590.0000.0640.0680.0000.1660.0750.0980.0000.0710.3410.041
anio_preinscripcion0.1240.1421.0001.0000.0000.9490.0000.0480.0620.0580.0160.0440.9260.0000.1350.000
antiguedad0.1370.1511.0001.0000.0760.949-0.1030.1020.3950.0000.0880.0840.9260.2570.1340.146
cant_Apoderado0.0000.0590.0000.0761.0000.5340.2470.5190.0930.0410.5630.1680.9130.0870.0000.031
cant_antecedentes0.4620.0000.9490.9490.5341.0000.0001.0000.0000.0000.5530.0000.2751.0000.0000.000
cant_autenticado0.0530.0640.000-0.1030.2470.0001.0000.082-0.0210.1480.3050.0060.275-0.0100.000-0.000
cant_noAutenticado0.0690.0680.0480.1020.5191.0000.0821.0000.0700.2870.7630.2101.0000.1110.0190.016
cant_procesos_adjudicado0.0000.0000.0620.3950.0930.000-0.0210.0701.000-0.0280.0850.0690.0000.5970.0000.303
cant_representante0.0320.1660.0580.0000.0410.0000.1480.287-0.0281.0000.2330.1440.000-0.0240.0000.008
cant_sinMontoLimite0.0050.0750.0160.0880.5630.5530.3050.7630.0850.2331.0000.1580.9260.1180.0000.011
cant_socios0.0000.0980.0440.0840.1680.0000.0060.2100.0690.1440.1581.0000.0000.1210.0000.011
cant_suspensiones0.0000.0000.9260.9260.9130.2750.2751.0000.0000.0000.9260.0001.0001.0000.0001.000
monto_total_adjudicado0.0000.0710.0000.2570.0871.000-0.0100.1110.597-0.0240.1180.1211.0001.0000.0000.079
provincia0.1900.3410.1350.1340.0000.0000.0000.0190.0000.0000.0000.0000.0000.0001.0000.000
total_articulos_provee0.0000.0410.0000.1460.0310.000-0.0000.0160.3030.0080.0110.0111.0000.0790.0001.000

Missing values

2025-06-24T10:33:15.192892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-24T10:33:15.382240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-24T10:33:15.584432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveecluster_k_4
820305924076Suministros EDA13/10/2016InscriptoPersona Física2016147.09.393091e+075.0Ciudad Autónoma de Buenos AiresNaNNaNNaNNaN2.0NaN1.01.02.0NaN107.03
1430710210000TECNARAN SRL06/09/2016InscriptoSociedad Responsabilidad Limitada201619.01.289448e+085.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaN1.01.02.0NaN2.0NaN1.03
1533714924619SIGNIFY ARGENTINA S.A.18/10/2016InscriptoSociedad Anónima20161.01.943875e+075.0Buenos Aires4.0NaNNaNNaN2.01.01.02.03.0NaN1.03
1733561600959Rognoni y CIA SA21/09/2016InscriptoSociedad Anónima20164.02.651773e+065.0Buenos Aires2.0NaNNaNNaNNaN2.02.0NaN2.0NaN71.03
1930694465591ADSUR S.A..22/09/2016InscriptoSociedad Anónima20166.04.605003e+075.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.01.01.01.02.0NaN1.03
2530678221976FÁBRICA ARGENTINA DE AVIONES "BRIG. SAN MARTÍN" S.A..17/11/2016InscriptoSociedad Anónima201663.01.917566e+105.0Córdoba4.0NaNNaNNaN1.0NaN1.0NaN1.0NaN14.03
2630707870571BASESIDE S.R.L.19/08/2016InscriptoSociedad Responsabilidad Limitada201620.08.117709e+065.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.01.01.01.02.0NaN6.03
2830708995157Netlabs SRL24/08/2016Desactualizado Por Documentos VencidosSociedad Responsabilidad Limitada20163.03.583917e+065.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.01.01.01.02.0NaN237.03
3130707480714M. B. G. COMERCIAL S. R. L.03/08/2016InscriptoSociedad Responsabilidad Limitada2016468.02.785304e+085.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaNNaN2.02.0NaN2.0NaN573.03
3330708085207TNGROUP S.A.16/09/2016InscriptoSociedad Anónima201639.02.372857e+095.0Ciudad Autónoma de Buenos Aires3.0NaNNaNNaN1.01.02.0NaN2.0NaN33.03
CUITNombreFechaPreinscripcionEstadoTipoSocietarioanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveecluster_k_4
1003030642796131PERFIL S.R.L.29/07/2020InscriptoSociedad Responsabilidad Limitada20201.01.037790e+061.0Neuquén7.0NaNNaNNaN1.01.02.0NaN2.0NaN25.03
1003830527725247SODA DI MARCO S.R.L.16/10/2018InscriptoSociedad Responsabilidad Limitada20181.06.557143e+043.0Mendoza3.0NaNNaNNaN2.0NaN2.0NaN2.0NaN3.03
1004030674562620INAR VIAL S.A.22/08/2022InscriptoSociedad Anónima20222.07.810812e+060.0Santa Fe5.0NaNNaNNaN1.04.01.04.05.0NaN1.03
1004120086498295sin datos20/12/2021InscriptoPersona Física20211.06.838163e+050.0La RiojaNaNNaNNaNNaN2.0NaN2.0NaN2.0NaN5.03
1004330713790407PEDRO GOITIA S.R.L.20/03/2017InscriptoSociedad Responsabilidad Limitada20171.01.997880e+074.0Ciudad Autónoma de Buenos Aires3.0NaNNaNNaN1.01.02.0NaN2.0NaN9.03
1004430708304472DROGUERIA GENESIS S.A23/02/2017InscriptoSociedad Anónima20172.01.026534e+074.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaNNaN2.02.0NaN2.0NaN832.03
1005130707885595Dirsin Corporation S.A.23/02/2018Desactualizado Por Mantencion FormularioSociedad Anónima20181.03.166163e+073.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaN1.01.02.0NaN2.0NaN1.03
1005720414755632sin datos13/09/2022InscriptoPersona Física20221.02.145238e+060.0ChubutNaNNaNNaNNaN2.0NaN1.01.02.0NaN1.03
1006220149549987TRANSPORTES ROBOL07/02/2017Desactualizado Por Mantencion FormularioPersona Física20171.05.261905e+064.0CorrientesNaNNaNNaNNaN2.0NaN2.0NaN2.0NaN4.03
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